A data fusion concept for a query language for multiple data sources

2000 ◽  
Author(s):  
E. Jungert
Keyword(s):  
Data Fusion ◽  
Query Language ◽  
Data Sources ◽  
Multiple Data Sources ◽  
Multiple Data ◽  
Fusion Concept

A SPATIAL/TEMPORAL QUERY LANGUAGE FOR MULTIPLE DATA SOURCES IN A HETEROGENEOUS INFORMATION SYSTEM ENVIRONMENT

1998 ◽  
Vol 07 (02n03) ◽  
pp. 167-186 ◽  
Author(s):  
SHI-KUO CHANG ◽  
ERLAND JUNGERT
Keyword(s):  
Information System ◽  
Virtual World ◽  
Query Language ◽  
Data Sources ◽  
Heterogeneous Information ◽  
Multiple Data Sources ◽  
Multiple Data ◽  
Temporal Query ◽  
Operator Sequence ◽  
Temporal Query Language

To support the retrieval, fusion and discovery of multimedia information, a spatial/temporal query language for multiple data sources is needed. In this paper we describe a spatial/temporal query language, the ∑QL, which is based upon the σ-operator sequence and in practice expressible in an SQL-like syntax. The general σ-operator and temporal σ-operator are explained, and applications of the σ-query language to vertical/horizontal reasoning and hypermapped virtual world are discussed.

scholarly journals Assessment of Data Fusion Oriented on Data Mining Approaches to Enhance Precision Agriculture Practices aimed at Increase of Durum Wheat (Triticum turgidum L. var. durum) Yield.

2018 ◽  
Vol 1 (1) ◽  
pp. 47
Author(s):  
A. D’Accolti ◽  
S. Maggio ◽  
A. Massaro ◽  
A.M. Galiano ◽  
V. Birardi ◽  
...  
Keyword(s):  
Data Mining ◽  
Data Fusion ◽  
Durum Wheat ◽  
Precision Agriculture ◽  
Triticum Turgidum ◽  
Data Sources ◽  
Software Systems ◽  
World Population ◽  
Multiple Data Sources ◽  
Multiple Data

In 2050, world population will reach a total of 9 billion inhabitants and their food demand have to be satisfied. Durum wheat (Triticum turgidum L. var. durum) is one of the most important food crop and its consumption is increasing worldwide. Productivity growth in agriculture and profitable returns are strongly influenced by investment in research and development, where Precision Agriculture (PA) represents an innovative way to manage farms by introducing the Information and Communication Technology (ICT) into the production process. It is known that farms activities produce large amounts of data. Today ICT allows, with electronic and software systems, to collect and transfer automatically these data, thus increasing yields and profits. In this direction significant data are processed from agricultural production, and retrieved to extract useful information important to increase the knowledge base. Data from multiple data sources can be processed by a Data Fusion (DF) approach able to combine multiple data sources into an unique database system. Raw data are transformed into useful information, thus DF improves pattern recognition, analysis of growth factors, and relationship between crops and environments. Data Fusion is synonym of Data Integration, Sensor Fusion, and Image Fusion. By means of Data Mining (DM) it is possible to extract useful information from data of the production processes thus providing new outputs concerning product quality and product “health status”. The following literature take into account the DF and DM techniques applied to Precision Agriculture (PA) and to cultivation inputs (water, nitrogen, etc.) management.  We report also last advances of DF and DM in modern agriculture and in precision durum wheat production.

scholarly journals A hands-on introduction to querying evolutionary relationships across multiple data sources using SPARQL

F1000Research ◽  
2020 ◽  
Vol 8 ◽  
pp. 1822
Author(s):  
Ana Claudia Sima ◽  
Christophe Dessimoz ◽  
Kurt Stockinger ◽  
Monique Zahn-Zabal ◽  
Tarcisio Mendes de Farias
Keyword(s):  
Query Language ◽  
Data Sources ◽  
Semantic Web Technologies ◽  
Genome Database ◽  
Steep Learning Curve ◽  
Web Technologies ◽  
Multiple Data Sources ◽  
Multiple Data ◽  
Hands On ◽  
Description Framework

The increasing use of Semantic Web technologies in the life sciences, in particular the use of the Resource Description Framework (RDF) and the RDF query language SPARQL, opens the path for novel integrative analyses, combining information from multiple data sources. However, analyzing evolutionary data in RDF is not trivial, due to the steep learning curve required to understand both the data models adopted by different RDF data sources, as well as the equivalent SPARQL constructs required to benefit from this data – in particular, recursive property paths. In this article, we provide a hands-on introduction to querying evolutionary data across several data sources that publish orthology information in RDF, namely: The Orthologous MAtrix (OMA), the European Bioinformatics Institute (EBI) RDF platform, the Database of Orthologous Groups (OrthoDB) and the Microbial Genome Database (MBGD). We present four protocols in increasing order of complexity. In these protocols, we demonstrate through SPARQL queries how to retrieve pairwise orthologs, homologous groups, and hierarchical orthologous groups. Finally, we show how orthology information in different data sources can be compared, through the use of federated SPARQL queries.

scholarly journals Integrating multiple data sources in species distribution modeling: a framework for data fusion*

Ecology ◽  
2017 ◽  
Vol 98 (3) ◽  
pp. 840-850 ◽  
Author(s):  
Krishna Pacifici ◽  
Brian J. Reich ◽  
David A. W. Miller ◽  
Beth Gardner ◽  
Glenn Stauffer ◽  
...  
Keyword(s):  
Data Fusion ◽  
Species Distribution ◽  
Species Distribution Modeling ◽  
Data Sources ◽  
Multiple Data Sources ◽  
Distribution Modeling ◽  
Multiple Data

scholarly journals Examining Deep Learning Models with Multiple Data Sources for COVID-19 Forecasting

2020 ◽  
Author(s):  
Lijing Wang ◽  
Aniruddha Adiga ◽  
Srinivasan Venkatramanan ◽  
Jiangzhuo Chen ◽  
Bryan Lewis ◽  
...  
Keyword(s):  
Deep Learning ◽  
Data Sources ◽  
Learning Models ◽  
Multiple Data Sources ◽  
Multiple Data

Big data and portfolio optimization: A novel approach integrating DEA with multiple data sources

Omega ◽  
2021 ◽  
pp. 102479
Author(s):  
Zhongbao Zhou ◽  
Meng Gao ◽  
Helu Xiao ◽  
Rui Wang ◽  
Wenbin Liu
Keyword(s):  
Big Data ◽  
Portfolio Optimization ◽  
Data Sources ◽  
Multiple Data Sources ◽  
Multiple Data ◽  
Novel Approach
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